Merge pull request #1111 from SciML/testsplit

Split QA tests to a separate group

Author: ChrisRackauckas

.github/workflows/CI.yml

@@ -24,6 +24,7 @@ jobs:
           - Core5
           - Core6
           - Core7
+          - QA
           - SDE1
           - SDE2
           - SDE3

src/forward_sensitivity.jl

@@ -157,7 +157,8 @@ function ODEForwardSensitivityProblem(f::F, args...; kwargs...) where {F}
     ODEForwardSensitivityProblem(ODEFunction(f), args...; kwargs...)
 end
 
-function ODEForwardSensitivityProblem(prob::ODEProblem; sensealg = ForwardSensitivity(), kwargs...)
+function ODEForwardSensitivityProblem(
+        prob::ODEProblem; sensealg = ForwardSensitivity(), kwargs...)
     _ODEForwardSensitivityProblem(
         prob.f, state_values(prob), prob.tspan, parameter_values(prob), sensealg; kwargs...)
 end
@@ -351,11 +352,10 @@ at time `sol.t[i]`. Note that all the functionality available to ODE solutions
 is available in this case, including interpolations and plot recipes (the recipes
 will plot the expanded system).
 """
-function ODEForwardSensitivityProblem(f::F, u0, tspan, p = nothing; 
-    sensealg = ForwardSensitivity(),
-    kwargs...) where {F <: DiffEqBase.AbstractODEFunction}
-
-    _ODEForwardSensitivityProblem(f,u0,tspan,p,sensealg; kwargs...)
+function ODEForwardSensitivityProblem(f::F, u0, tspan, p = nothing;
+        sensealg = ForwardSensitivity(),
+        kwargs...) where {F <: DiffEqBase.AbstractODEFunction}
+    _ODEForwardSensitivityProblem(f, u0, tspan, p, sensealg; kwargs...)
 end
 
 # deprecated
@@ -366,8 +366,10 @@ function ODEForwardSensitivityProblem(f::F, u0,
         w0 = nothing,
         v0 = nothing,
         kwargs...) where {F <: DiffEqBase.AbstractODEFunction}
-    Base.depwarn("The form of this function with `alg` as a positional argument is deprecated. Please use the `sensealg` keyword argument instead.", :ODEForwardSensitivityProblem)
-    _ODEForwardSensitivityProblem(f,u0,tspan,p,alg; nus,w0,v0,kwargs...)    
+    Base.depwarn(
+        "The form of this function with `alg` as a positional argument is deprecated. Please use the `sensealg` keyword argument instead.",
+        :ODEForwardSensitivityProblem)
+    _ODEForwardSensitivityProblem(f, u0, tspan, p, alg; nus, w0, v0, kwargs...)
 end
 
 function _ODEForwardSensitivityProblem(f::F, u0,
@@ -490,7 +492,9 @@ function ODEForwardSensitivityProblem(f::DiffEqBase.AbstractODEFunction, u0,
         du0 = zeros(eltype(u0), length(u0), length(p)), # perturbations of initial condition
         dp = I(length(p)), # perturbations of parameters
         kwargs...)
-    Base.depwarn("The form of this function with `alg` as a positional argument is deprecated. Please use the `sensealg` keyword argument instead.", :ODEForwardSensitivity)
+    Base.depwarn(
+        "The form of this function with `alg` as a positional argument is deprecated. Please use the `sensealg` keyword argument instead.",
+        :ODEForwardSensitivity)
     _ODEForwardSensitivityProblem(f, u0, tspan, p, alg, du0, dp, kwargs...)
 end
 

test/forward.jl

@@ -37,7 +37,8 @@ probvecmat = ODEForwardSensitivityProblem(fb, [1.0; 1.0], (0.0, 10.0), p,
         autojacmat = true))
 
 # tests that the deprecated version still works
-dep_prob_const = ODEForwardSensitivityProblem(fb, [1.0; 1.0], (0.0, 10.0), p, ForwardSensitivity())
+dep_prob_const = ODEForwardSensitivityProblem(
+    fb, [1.0; 1.0], (0.0, 10.0), p, ForwardSensitivity())
 
 sol = solve(prob, Tsit5(), abstol = 1e-14, reltol = 1e-14)
 @test_broken solve(probInpl, KenCarp4(), abstol = 1e-14, reltol = 1e-14).retcode == :Success
@@ -199,7 +200,8 @@ sol_MM_ForwardDiffSensitivity = solve(prob_MM_ForwardDiffSensitivity,
     Rodas4(autodiff = false), reltol = 1e-14,
     abstol = 1e-14)
 
-prob_no_MM = ODEForwardSensitivityProblem(f_no_MM, u0, tspan, p, sensealg = ForwardSensitivity())
+prob_no_MM = ODEForwardSensitivityProblem(
+    f_no_MM, u0, tspan, p, sensealg = ForwardSensitivity())
 sol_no_MM = solve(prob_no_MM, Rodas4(autodiff = false), reltol = 1e-14, abstol = 1e-14)
 
 sen_MM_ForwardSensitivity = extract_local_sensitivities(sol_MM_ForwardSensitivity, 10.0,

test/noindex_tests.jl

@@ -7,10 +7,12 @@ end
 Base.size(x::CustomArray) = size(x.x)
 Base.axes(x::CustomArray) = axes(x.x)
 Base.ndims(x::CustomArray) = ndims(x.x)
-Base.ndims(::Type{<:CustomArray{T,N}}) where {T,N} = N
+Base.ndims(::Type{<:CustomArray{T, N}}) where {T, N} = N
 Base.zero(x::CustomArray) = CustomArray(zero(x.x))
-Base.zero(::Type{<:CustomArray{T,N}}) where {T,N} = CustomArray(zero(Array{T,N}))
-Base.similar(x::CustomArray, dims::Union{Integer, AbstractUnitRange}...) = CustomArray(similar(x.x, dims...))
+Base.zero(::Type{<:CustomArray{T, N}}) where {T, N} = CustomArray(zero(Array{T, N}))
+function Base.similar(x::CustomArray, dims::Union{Integer, AbstractUnitRange}...)
+    CustomArray(similar(x.x, dims...))
+end
 Base.copyto!(x::CustomArray, y::CustomArray) = CustomArray(copyto!(x.x, y.x))
 Base.copy(x::CustomArray) = CustomArray(copy(x.x))
 Base.length(x::CustomArray) = length(x.x)
@@ -29,22 +31,30 @@ Base.all(f::Function, x::CustomArray; kwargs...) = all(f, x.x; kwargs...)
 Base.similar(x::CustomArray, t) = CustomArray(similar(x.x, t))
 Base.:(+)(x::CustomArray, y::CustomArray) = CustomArray(x.x + y.x)
 Base.:(==)(x::CustomArray, y::CustomArray) = x.x == y.x
-Base.:(*)(x::Number, y::CustomArray) = CustomArray(x*y.x)
-Base.:(/)(x::CustomArray, y::Number) = CustomArray(x.x/y)
+Base.:(*)(x::Number, y::CustomArray) = CustomArray(x * y.x)
+Base.:(/)(x::CustomArray, y::Number) = CustomArray(x.x / y)
 LinearAlgebra.norm(x::CustomArray) = norm(x.x)
 LinearAlgebra.vec(x::CustomArray) = CustomArray(vec(x.x))
 
 struct CustomStyle{N} <: Broadcast.BroadcastStyle where {N} end
-CustomStyle(::Val{N}) where N = CustomStyle{N}()
-CustomStyle{M}(::Val{N}) where {N,M} = NoIndexStyle{N}()
-Base.BroadcastStyle(::Type{<:CustomArray{T,N}}) where {T,N} = CustomStyle{N}()
-Broadcast.BroadcastStyle(::CustomStyle{N}, ::Broadcast.DefaultArrayStyle{0}) where {N} = CustomStyle{N}()
-Base.similar(bc::Base.Broadcast.Broadcasted{CustomStyle{N}}, ::Type{ElType}) where {N, ElType} = CustomArray(similar(Array{ElType, N}, axes(bc)))
+CustomStyle(::Val{N}) where {N} = CustomStyle{N}()
+CustomStyle{M}(::Val{N}) where {N, M} = NoIndexStyle{N}()
+Base.BroadcastStyle(::Type{<:CustomArray{T, N}}) where {T, N} = CustomStyle{N}()
+function Broadcast.BroadcastStyle(
+        ::CustomStyle{N}, ::Broadcast.DefaultArrayStyle{0}) where {N}
+    CustomStyle{N}()
+end
+function Base.similar(
+        bc::Base.Broadcast.Broadcasted{CustomStyle{N}}, ::Type{ElType}) where {N, ElType}
+    CustomArray(similar(Array{ElType, N}, axes(bc)))
+end
 Base.@propagate_inbounds Base.Broadcast._broadcast_getindex(x::CustomArray, i) = x.x[i]
 Base.Broadcast.extrude(x::CustomArray) = x
 Base.Broadcast.broadcastable(x::CustomArray) = x
 
-@inline function Base.copyto!(dest::CustomArray, bc::Base.Broadcast.Broadcasted{<:Union{Base.Broadcast.AbstractArrayStyle,CustomStyle}})
+@inline function Base.copyto!(dest::CustomArray,
+        bc::Base.Broadcast.Broadcasted{<:Union{
+            Base.Broadcast.AbstractArrayStyle, CustomStyle}})
     axes(dest) == axes(bc) || throwdm(axes(dest), axes(bc))
     bc′ = Base.Broadcast.preprocess(dest, bc)
     dest′ = dest.x
@@ -75,7 +85,7 @@ RecursiveArrayTools.recursivefill!(x::CustomArray, a) = fill!(x, a)
 
 Base.show_vector(io::IO, x::CustomArray) = Base.show_vector(io, x.x)
 
-Base.show(io::IO, x::CustomArray) = (print(io, "CustomArray");show(io, x.x))
+Base.show(io::IO, x::CustomArray) = (print(io, "CustomArray"); show(io, x.x))
 function Base.show(io::IO, ::MIME"text/plain", x::CustomArray)
     println(io, Base.summary(x), ":")
     Base.print_array(io, x.x)
@@ -89,9 +99,11 @@ algs = [Tsit5(), BS3(), Vern9(), DP5()]
 
 for alg in algs
     function cost(p)
-        prob = ODEProblem((du, u, p, t) -> (du[1] = p[1]*u[1] + p[2]*u[2]; du[2] = p[2]*u[1]), ca0, tspan, p)
+        prob = ODEProblem(
+            (du, u, p, t) -> (du[1] = p[1] * u[1] + p[2] * u[2]; du[2] = p[2] * u[1]),
+            ca0, tspan, p)
         sol = solve(prob, alg; save_everystep = false)
         return 1 - norm(sol[end])^2
     end
     @test_nowarn Zygote.gradient(cost, par)
-end
+end

test/runtests.jl

@@ -74,7 +74,6 @@ end
             @time @safetestset "ForwardDiff Sparsity Components" include("forwarddiffsensitivity_sparsity_components.jl")
             @time @safetestset "Complex No u" include("complex_no_u.jl")
             @time @safetestset "Parameter Handling" include("parameter_handling.jl")
-            @time @safetestset "Quality Assurance" include("aqua.jl")
         end
     end
 
@@ -101,6 +100,10 @@ end
         end
     end
 
+    if GROUP == "All" || GROUP == "QA"
+        @time @safetestset "Quality Assurance" include("aqua.jl")
+    end
+
     if GROUP == "All" || GROUP == "SDE1"
         @testset "SDE 1" begin
             @time @safetestset "SDE Adjoint" include("sde_stratonovich.jl")

test/sde_transformation_test.jl

@@ -45,7 +45,9 @@ prob_strat = SDEProblem{false}(
     tspan,
     p)
 Random.seed!(seed)
-sol_strat = solve(prob_strat, RKMil(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich), adaptive = false,
+sol_strat = solve(
+    prob_strat, RKMil(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich),
+    adaptive = false,
     dt = 0.0001, save_noise = true)
 prob_strat1 = SDEProblem{false}(
     SDEFunction((u, p, t) -> transformed_function(u, p, t) .+
@@ -56,7 +58,9 @@ prob_strat1 = SDEProblem{false}(
     tspan,
     p)
 Random.seed!(seed)
-sol_strat1 = solve(prob_strat1, RKMil(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich), adaptive = false,
+sol_strat1 = solve(
+    prob_strat1, RKMil(interpretation = SciMLBase.AlgorithmInterpretation.Stratonovich),
+    adaptive = false,
     dt = 0.0001, save_noise = true)
 
 # Test if we recover Ito solution in Stratonovich sense